用于敌对环境的鲁棒高效群通信拓扑
Robust and Efficient Swarm Communication Topologies for Hostile Environments
摘要：
基于群体智能的优化技术将搜索空间的系统探索与从邻居获得的信息相结合，并且强烈依赖于代理之间的通信。这些算法通常用于解决函数景观不充分已知的问题，并且存在多个局部最优解，这可能导致其他算法过早收敛。这种算法的应用可以在通信系统中找到，包括设计网络以向目标群体有效传播信息，靶向药物递送，其中药物分子在扩散之前搜索受影响的部位，以及利用无人机网络进行高价值目标定位。在几个这样的应用程序中，代理面临着一个敌对的环境，可能会导致在搜索过程中的代理丢失。这样的损失改变了代理的通信拓扑结构，从而改变了代理可用的信息，最终影响算法的性能。在本文中，我们提出了一个研究的影响，损失的代理对这些算法的性能作为一个功能的初始网络配置。我们使用粒子群优化优化目标函数与多个次优区域在恶劣的环境中，并研究其性能的一系列网络拓扑结构与损失的代理。结果揭示了有趣的效率，鲁棒性和性能之间的权衡，随后利用不同的拓扑结构，发现网络的一般属性，最大限度地提高性能。此外，具有小世界属性的网络被认为可以在恶劣条件下最大限度地提高性能。
英文摘要：
Swarm Intelligence-based optimization techniques combine systematic exploration of the search space with information available from neighbors and rely strongly on communication among agents. These algorithms are typically employed to solve problems where the function landscape is not adequately known and there are multiple local optima that could result in premature convergence for other algorithms. Applications of such algorithms can be found in communication systems involving design of networks for efficient information dissemination to a target group, targeted drug-delivery where drug molecules search for the affected site before diffusing, and high-value target localization with a network of drones. In several of such applications, the agents face a hostile environment that can result in loss of agents during the search. Such a loss changes the communication topology of the agents and hence the information available to agents, ultimately influencing the performance of the algorithm. In this paper, we present a study of the impact of loss of agents on the performance of such algorithms as a function of the initial network configuration. We use particle swarm optimization to optimize an objective function with multiple sub-optimal regions in a hostile environment and study its performance for a range of network topologies with loss of agents. The results reveal interesting trade-offs between efficiency, robustness, and performance for different topologies that are subsequently leveraged to discover general properties of networks that maximize performance. Moreover, networks with small-world properties are seen to maximize performance under hostile conditions.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
